Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells6447
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 3 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 382 (1.1%) missing valuesMissing
SO2 has 575 (1.6%) missing valuesMissing
NO2 has 1070 (3.1%) missing valuesMissing
CO has 1812 (5.2%) missing valuesMissing
O3 has 2107 (6.0%) missing valuesMissing
RAIN is highly skewed (γ1 = 34.94218413)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33608 (95.8%) zerosZeros
WSPM has 1640 (4.7%) zerosZeros

Reproduction

Analysis started2024-03-08 05:16:24.293612
Analysis finished2024-03-08 05:17:03.793354
Duration39.5 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:03.903634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:17:04.130495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:17:04.313890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:17:04.529256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:04.748319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:17:04.928191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:05.122990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:17:05.356337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:05.541512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:17:05.767032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct539
Distinct (%)1.6%
Missing382
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean83.374716
Minimum2
Maximum957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:05.997369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q123
median59
Q3116
95-th percentile247
Maximum957
Range955
Interquartile range (IQR)93

Descriptive statistics

Standard deviation81.905568
Coefficient of variation (CV)0.98237898
Kurtosis5.3430366
Mean83.374716
Median Absolute Deviation (MAD)41
Skewness1.9224929
Sum2891601.9
Variance6708.5221
MonotonicityNot monotonic
2024-03-08T12:17:06.198102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 691
 
2.0%
10 503
 
1.4%
8 501
 
1.4%
11 486
 
1.4%
12 480
 
1.4%
9 479
 
1.4%
14 463
 
1.3%
13 453
 
1.3%
7 444
 
1.3%
6 436
 
1.2%
Other values (529) 29746
84.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 691
2.0%
4 295
0.8%
5 334
1.0%
6 436
1.2%
7 444
1.3%
8 501
1.4%
9 479
1.4%
10 503
1.4%
11 486
1.4%
ValueCountFrequency (%)
957 1
< 0.1%
791 1
< 0.1%
770 1
< 0.1%
718 1
< 0.1%
708 1
< 0.1%
692 1
< 0.1%
664 1
< 0.1%
661 1
< 0.1%
657 1
< 0.1%
638 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)1.8%
Missing284
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean110.46462
Minimum2
Maximum951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:06.463372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q140
median88
Q3153
95-th percentile289
Maximum951
Range949
Interquartile range (IQR)113

Descriptive statistics

Standard deviation92.795065
Coefficient of variation (CV)0.84004333
Kurtosis4.7095494
Mean110.46462
Median Absolute Deviation (MAD)54
Skewness1.6964547
Sum3841959.4
Variance8610.9241
MonotonicityNot monotonic
2024-03-08T12:17:06.735856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 333
 
0.9%
16 292
 
0.8%
17 287
 
0.8%
18 285
 
0.8%
14 285
 
0.8%
24 282
 
0.8%
15 274
 
0.8%
23 270
 
0.8%
21 269
 
0.8%
20 268
 
0.8%
Other values (606) 31935
91.1%
(Missing) 284
 
0.8%
ValueCountFrequency (%)
2 14
 
< 0.1%
3 34
 
0.1%
4 18
 
0.1%
5 230
0.7%
6 333
0.9%
7 169
0.5%
8 207
0.6%
9 205
0.6%
10 221
0.6%
11 225
0.6%
ValueCountFrequency (%)
951 1
< 0.1%
923 1
< 0.1%
919 1
< 0.1%
893 1
< 0.1%
845 1
< 0.1%
829 1
< 0.1%
806 1
< 0.1%
792 1
< 0.1%
785 1
< 0.1%
784 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct460
Distinct (%)1.3%
Missing575
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean18.376481
Minimum0.2856
Maximum282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:06.937052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q14
median10
Q323
95-th percentile65
Maximum282
Range281.7144
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.609648
Coefficient of variation (CV)1.2303579
Kurtosis10.415817
Mean18.376481
Median Absolute Deviation (MAD)8
Skewness2.7266078
Sum633786.44
Variance511.19617
MonotonicityNot monotonic
2024-03-08T12:17:07.210459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6186
 
17.6%
3 1503
 
4.3%
4 1306
 
3.7%
6 1258
 
3.6%
7 1239
 
3.5%
5 1192
 
3.4%
9 1178
 
3.4%
8 1149
 
3.3%
10 1122
 
3.2%
11 911
 
2.6%
Other values (450) 17445
49.8%
ValueCountFrequency (%)
0.2856 3
 
< 0.1%
0.5712 3
 
< 0.1%
0.8568 5
 
< 0.1%
1 2
 
< 0.1%
1.1424 12
 
< 0.1%
1.428 10
 
< 0.1%
1.7136 13
 
< 0.1%
1.9992 16
 
< 0.1%
2 6186
17.6%
2.2848 27
 
0.1%
ValueCountFrequency (%)
282 1
< 0.1%
225 1
< 0.1%
221 1
< 0.1%
216 1
< 0.1%
215 1
< 0.1%
207 1
< 0.1%
204 1
< 0.1%
197 1
< 0.1%
194 1
< 0.1%
193 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct958
Distinct (%)2.8%
Missing1070
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean65.258789
Minimum1.6424
Maximum264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:07.460871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6424
5-th percentile14
Q136
median60
Q388
95-th percentile135
Maximum264
Range262.3576
Interquartile range (IQR)52

Descriptive statistics

Standard deviation37.996088
Coefficient of variation (CV)0.5822371
Kurtosis0.80462239
Mean65.258789
Median Absolute Deviation (MAD)26
Skewness0.83624111
Sum2218407.3
Variance1443.7027
MonotonicityNot monotonic
2024-03-08T12:17:07.651386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 347
 
1.0%
34 340
 
1.0%
41 338
 
1.0%
40 333
 
0.9%
47 326
 
0.9%
50 325
 
0.9%
32 325
 
0.9%
26 323
 
0.9%
44 321
 
0.9%
56 316
 
0.9%
Other values (948) 30700
87.6%
(Missing) 1070
 
3.1%
ValueCountFrequency (%)
1.6424 1
 
< 0.1%
2 104
0.3%
2.2583 1
 
< 0.1%
2.8742 1
 
< 0.1%
3 15
 
< 0.1%
4 27
 
0.1%
4.106 1
 
< 0.1%
4.5166 1
 
< 0.1%
4.7219 1
 
< 0.1%
4.9272 1
 
< 0.1%
ValueCountFrequency (%)
264 1
< 0.1%
263 1
< 0.1%
259 1
< 0.1%
257 1
< 0.1%
253 2
< 0.1%
252 1
< 0.1%
251 2
< 0.1%
250 1
< 0.1%
249 1
< 0.1%
248 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct120
Distinct (%)0.4%
Missing1812
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean1319.3535
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:07.864965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1500
median900
Q31600
95-th percentile4000
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1268.1143
Coefficient of variation (CV)0.96116342
Kurtosis7.3300943
Mean1319.3535
Median Absolute Deviation (MAD)400
Skewness2.4073928
Sum43871143
Variance1608114
MonotonicityNot monotonic
2024-03-08T12:17:08.085274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 2475
 
7.1%
300 2418
 
6.9%
700 2386
 
6.8%
500 2227
 
6.4%
400 2185
 
6.2%
800 2125
 
6.1%
900 1924
 
5.5%
1000 1704
 
4.9%
1100 1544
 
4.4%
1200 1290
 
3.7%
Other values (110) 12974
37.0%
(Missing) 1812
 
5.2%
ValueCountFrequency (%)
100 513
 
1.5%
200 1199
3.4%
300 2418
6.9%
400 2185
6.2%
500 2227
6.4%
600 2475
7.1%
700 2386
6.8%
800 2125
6.1%
900 1924
5.5%
1000 1704
4.9%
ValueCountFrequency (%)
10000 7
< 0.1%
9900 1
 
< 0.1%
9800 4
< 0.1%
9700 1
 
< 0.1%
9600 3
< 0.1%
9500 1
 
< 0.1%
9400 2
 
< 0.1%
9300 4
< 0.1%
9200 2
 
< 0.1%
9100 3
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct734
Distinct (%)2.2%
Missing2107
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean48.873614
Minimum0.2142
Maximum364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:08.259829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q14
median32
Q373
95-th percentile167
Maximum364
Range363.7858
Interquartile range (IQR)69

Descriptive statistics

Standard deviation55.11174
Coefficient of variation (CV)1.1276379
Kurtosis2.6728349
Mean48.873614
Median Absolute Deviation (MAD)30
Skewness1.5951624
Sum1610727.7
Variance3037.3039
MonotonicityNot monotonic
2024-03-08T12:17:08.518629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6228
 
17.8%
3 811
 
2.3%
1 804
 
2.3%
4 745
 
2.1%
5 552
 
1.6%
6 507
 
1.4%
7 365
 
1.0%
8 344
 
1.0%
9 287
 
0.8%
12 284
 
0.8%
Other values (724) 22030
62.8%
(Missing) 2107
 
6.0%
ValueCountFrequency (%)
0.2142 50
 
0.1%
0.4284 38
 
0.1%
0.6426 42
 
0.1%
0.8568 20
 
0.1%
1 804
2.3%
1.071 17
 
< 0.1%
1.2852 18
 
0.1%
1.4994 22
 
0.1%
1.7136 17
 
< 0.1%
1.9278 14
 
< 0.1%
ValueCountFrequency (%)
364 1
< 0.1%
357 2
< 0.1%
353 1
< 0.1%
352 1
< 0.1%
351 1
< 0.1%
347 1
< 0.1%
346 1
< 0.1%
344 2
< 0.1%
342 2
< 0.1%
339 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct971
Distinct (%)2.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.428865
Minimum-15.8
Maximum40.5
Zeros172
Zeros (%)0.5%
Negative5415
Negative (%)15.4%
Memory size274.1 KiB
2024-03-08T12:17:08.734922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-15.8
5-th percentile-4.17925
Q13.2
median14.3
Q322.9
95-th percentile30.6
Maximum40.5
Range56.3
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation11.346931
Coefficient of variation (CV)0.84496573
Kurtosis-1.1275469
Mean13.428865
Median Absolute Deviation (MAD)9.7
Skewness-0.085516299
Sum470601.15
Variance128.75284
MonotonicityNot monotonic
2024-03-08T12:17:09.205821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 228
 
0.7%
1 218
 
0.6%
-2 195
 
0.6%
2 194
 
0.6%
-1 173
 
0.5%
0 172
 
0.5%
21.7 165
 
0.5%
-4 149
 
0.4%
4 139
 
0.4%
23.6 138
 
0.4%
Other values (961) 33273
94.9%
ValueCountFrequency (%)
-15.8 1
 
< 0.1%
-15.5 1
 
< 0.1%
-15.3 1
 
< 0.1%
-15.2 1
 
< 0.1%
-15.1 1
 
< 0.1%
-15 3
< 0.1%
-14.9 2
< 0.1%
-14.8 2
< 0.1%
-14.7 1
 
< 0.1%
-14.5 2
< 0.1%
ValueCountFrequency (%)
40.5 1
< 0.1%
40.3 1
< 0.1%
40.1 1
< 0.1%
39.2 1
< 0.1%
39 1
< 0.1%
38.7 2
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38.1 1
< 0.1%
38 2
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct593
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1011.0975
Minimum985.9
Maximum1040.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:09.457651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum985.9
5-th percentile995.5
Q11002.5
median1010.8
Q31019.4
95-th percentile1028
Maximum1040.3
Range54.4
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation10.355247
Coefficient of variation (CV)0.01024159
Kurtosis-0.9135443
Mean1011.0975
Median Absolute Deviation (MAD)8.4
Skewness0.1095566
Sum35432902
Variance107.23113
MonotonicityNot monotonic
2024-03-08T12:17:09.696759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024 262
 
0.7%
1023 254
 
0.7%
1019 252
 
0.7%
1025 251
 
0.7%
1020 247
 
0.7%
1021 237
 
0.7%
1022 228
 
0.7%
1026 226
 
0.6%
1018 222
 
0.6%
1017 220
 
0.6%
Other values (583) 32645
93.1%
ValueCountFrequency (%)
985.9 1
< 0.1%
986 1
< 0.1%
986.1 1
< 0.1%
986.2 1
< 0.1%
986.3 2
< 0.1%
986.4 1
< 0.1%
986.5 1
< 0.1%
986.6 1
< 0.1%
986.7 1
< 0.1%
986.9 1
< 0.1%
ValueCountFrequency (%)
1040.3 1
 
< 0.1%
1040.2 1
 
< 0.1%
1039.9 1
 
< 0.1%
1039.7 2
< 0.1%
1039.6 2
< 0.1%
1039.5 3
< 0.1%
1039.3 4
< 0.1%
1039.2 1
 
< 0.1%
1038.9 2
< 0.1%
1038.8 3
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct600
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.2665877
Minimum-34.9
Maximum28.5
Zeros65
Zeros (%)0.2%
Negative14742
Negative (%)42.0%
Memory size274.1 KiB
2024-03-08T12:17:09.941720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-34.9
5-th percentile-19.1
Q1-8.1
median4
Q315.8
95-th percentile22.4
Maximum28.5
Range63.4
Interquartile range (IQR)23.9

Descriptive statistics

Standard deviation13.67806
Coefficient of variation (CV)4.1872623
Kurtosis-1.1234199
Mean3.2665877
Median Absolute Deviation (MAD)11.9
Skewness-0.21502917
Sum114474.3
Variance187.08931
MonotonicityNot monotonic
2024-03-08T12:17:10.166594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.2 145
 
0.4%
17.6 135
 
0.4%
17 130
 
0.4%
19.2 130
 
0.4%
18.3 129
 
0.4%
18.2 128
 
0.4%
18.8 127
 
0.4%
17.8 125
 
0.4%
16.7 125
 
0.4%
17.5 124
 
0.4%
Other values (590) 33746
96.2%
ValueCountFrequency (%)
-34.9 1
< 0.1%
-34.5 2
< 0.1%
-34 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
-33.5 1
< 0.1%
-32.9 1
< 0.1%
-32.6 1
< 0.1%
-32.5 2
< 0.1%
-32.3 1
< 0.1%
ValueCountFrequency (%)
28.5 1
 
< 0.1%
27.8 5
< 0.1%
27.7 1
 
< 0.1%
27.6 1
 
< 0.1%
27.5 3
< 0.1%
27.4 1
 
< 0.1%
27.3 2
 
< 0.1%
27.2 3
< 0.1%
27 5
< 0.1%
26.9 3
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct125
Distinct (%)0.4%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.068262755
Minimum0
Maximum72.5
Zeros33608
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:10.315027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89672555
Coefficient of variation (CV)13.136381
Kurtosis1893.365
Mean0.068262755
Median Absolute Deviation (MAD)0
Skewness34.942184
Sum2392.2
Variance0.80411671
MonotonicityNot monotonic
2024-03-08T12:17:10.512402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33608
95.8%
0.1 333
 
0.9%
0.2 173
 
0.5%
0.3 99
 
0.3%
0.4 84
 
0.2%
0.5 80
 
0.2%
0.6 59
 
0.2%
0.9 47
 
0.1%
0.7 47
 
0.1%
0.8 42
 
0.1%
Other values (115) 472
 
1.3%
ValueCountFrequency (%)
0 33608
95.8%
0.1 333
 
0.9%
0.2 173
 
0.5%
0.3 99
 
0.3%
0.4 84
 
0.2%
0.5 80
 
0.2%
0.6 59
 
0.2%
0.7 47
 
0.1%
0.8 42
 
0.1%
0.9 47
 
0.1%
ValueCountFrequency (%)
72.5 1
< 0.1%
47.7 1
< 0.1%
40.7 1
< 0.1%
38.9 1
< 0.1%
28.9 1
< 0.1%
26.8 1
< 0.1%
26 1
< 0.1%
25.9 1
< 0.1%
23.7 1
< 0.1%
23.6 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing123
Missing (%)0.4%
Memory size274.1 KiB
NE
6703 
SW
4644 
ENE
3642 
NNE
3327 
N
3219 
Other values (11)
13406 

Length

Max length3
Median length2
Mean length2.2350248
Min length1

Characters and Unicode

Total characters78094
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN

Common Values

ValueCountFrequency (%)
NE 6703
19.1%
SW 4644
13.2%
ENE 3642
10.4%
NNE 3327
9.5%
N 3219
9.2%
WSW 2529
 
7.2%
SSW 2101
 
6.0%
NNW 1397
 
4.0%
W 1335
 
3.8%
E 1264
 
3.6%
Other values (6) 4780
13.6%

Length

2024-03-08T12:17:10.715679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 6703
19.2%
sw 4644
13.3%
ene 3642
10.4%
nne 3327
9.5%
n 3219
9.2%
wsw 2529
 
7.2%
ssw 2101
 
6.0%
nnw 1397
 
4.0%
w 1335
 
3.8%
e 1264
 
3.6%
Other values (6) 4780
13.7%

Most occurring characters

ValueCountFrequency (%)
N 24932
31.9%
E 21044
26.9%
W 17268
22.1%
S 14850
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78094
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24932
31.9%
E 21044
26.9%
W 17268
22.1%
S 14850
19.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78094
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24932
31.9%
E 21044
26.9%
W 17268
22.1%
S 14850
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 24932
31.9%
E 21044
26.9%
W 17268
22.1%
S 14850
19.0%

WSPM
Real number (ℝ)

ZEROS 

Distinct86
Distinct (%)0.2%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.5012154
Minimum0
Maximum11.2
Zeros1640
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:17:10.957752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.8
median1.2
Q32
95-th percentile3.7
Maximum11.2
Range11.2
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1044721
Coefficient of variation (CV)0.73571863
Kurtosis2.7306412
Mean1.5012154
Median Absolute Deviation (MAD)0.6
Skewness1.3867358
Sum52617.6
Variance1.2198587
MonotonicityNot monotonic
2024-03-08T12:17:11.148145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1912
 
5.5%
1.1 1869
 
5.3%
1.2 1865
 
5.3%
0.9 1776
 
5.1%
0.8 1692
 
4.8%
0 1640
 
4.7%
1.3 1615
 
4.6%
0.7 1535
 
4.4%
1.4 1528
 
4.4%
0.6 1422
 
4.1%
Other values (76) 18196
51.9%
ValueCountFrequency (%)
0 1640
4.7%
0.1 668
 
1.9%
0.2 676
 
1.9%
0.3 486
 
1.4%
0.4 858
2.4%
0.5 1161
3.3%
0.6 1422
4.1%
0.7 1535
4.4%
0.8 1692
4.8%
0.9 1776
5.1%
ValueCountFrequency (%)
11.2 1
 
< 0.1%
9.1 1
 
< 0.1%
8.6 1
 
< 0.1%
8.4 1
 
< 0.1%
8.3 2
< 0.1%
8.1 1
 
< 0.1%
8 1
 
< 0.1%
7.9 1
 
< 0.1%
7.7 1
 
< 0.1%
7.6 4
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Wanliu
35064 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters210384
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWanliu
2nd rowWanliu
3rd rowWanliu
4th rowWanliu
5th rowWanliu

Common Values

ValueCountFrequency (%)
Wanliu 35064
100.0%

Length

2024-03-08T12:17:11.329996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:17:11.466157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
wanliu 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
W 35064
16.7%
a 35064
16.7%
n 35064
16.7%
l 35064
16.7%
i 35064
16.7%
u 35064
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175320
83.3%
Uppercase Letter 35064
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35064
20.0%
n 35064
20.0%
l 35064
20.0%
i 35064
20.0%
u 35064
20.0%
Uppercase Letter
ValueCountFrequency (%)
W 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 210384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 35064
16.7%
a 35064
16.7%
n 35064
16.7%
l 35064
16.7%
i 35064
16.7%
u 35064
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 35064
16.7%
a 35064
16.7%
n 35064
16.7%
l 35064
16.7%
i 35064
16.7%
u 35064
16.7%

Interactions

2024-03-08T12:17:00.092836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.063476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:28.947959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.295101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:33.738482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.035286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:38.930650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.050127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.481175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.913731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.427356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.802937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.084232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:55.381314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.752518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.222128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.249021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.084293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.465759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:33.882535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.217347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.075550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.195140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.669381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.101158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.591879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.938455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.263230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:55.737333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.935589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.364920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.386672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.221001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.575790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.019603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.361112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.270118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.347722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.802644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.283626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.737504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.156026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.425236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:55.898241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.064283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.526295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.544862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.399304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.715038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.198809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.500934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.420240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.483930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.957060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.499205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.946973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.318881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.602537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.065941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.267603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.677376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.695096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.542835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.867481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.338684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.658613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.562629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.633837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.160222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.648287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.108263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.451589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.757310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.176272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.431948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.828855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:26.876385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.698259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.032058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.500146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.782557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.738179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.825938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.328585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.793796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.233442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.583934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:53.891659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.308357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.566644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:00.976396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.012248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:29.870770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.173329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.659347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:36.960640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:39.898992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:41.976724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.461619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:46.928315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.355297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.738688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.023682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.456455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.735080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.113544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.222431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.005179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.303408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.817089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:37.147294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.023602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.146806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.622422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:47.055667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.516783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:51.899791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.225397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.592833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:58.902797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.263599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.416137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.173450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.481680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:34.982656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:37.325211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.148419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.281458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.804389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:47.207454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.693880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.039445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.411214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.733822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.045424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.506433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.590608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.343211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.636233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.159830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:37.549612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.280246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.426400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:44.957363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:47.553038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:49.859187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.194243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.512731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:56.880738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.221392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.678569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.787410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.508840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.830926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.293272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:37.715095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.423041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.599175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.079377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:47.708213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.036049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.340264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.655894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.035365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.393667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.833460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:27.981716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.651688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:32.992924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.423246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:38.307918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.539439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.746383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.239798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:47.851057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.150467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.469156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.782069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.168954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.518122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:01.981431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:28.205492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.778996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:33.183464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.583465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:38.476763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.673312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:42.915294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.399540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.005078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.349159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.643960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:54.912885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.300719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.656772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:02.117250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:28.391141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:30.960591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:33.332216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.732945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:38.647351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.812119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.069013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.572861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.160193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.492600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.793567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:55.110382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.440367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.792877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:02.257625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:28.559130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:31.153957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:33.549598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:35.888036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:38.791766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:40.938678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:43.318623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:45.753057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:48.298364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:50.662266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:52.943259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:55.259667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:57.599513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:16:59.932930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:17:11.608912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.0000.725-0.042-0.4660.7170.8170.157-0.0210.582-0.294-0.3350.003-0.0470.0100.0820.074
DEWP0.0001.000-0.068-0.1270.2380.1320.225-0.7840.176-0.3410.822-0.3050.025-0.0110.2550.1000.158
NO20.725-0.0681.000-0.151-0.6270.6880.6730.147-0.1190.552-0.307-0.3880.020-0.034-0.0510.1130.116
No-0.042-0.127-0.1511.0000.033-0.135-0.1190.1710.012-0.359-0.120-0.0360.0180.0010.0440.1060.862
O3-0.4660.238-0.6270.0331.000-0.264-0.267-0.411-0.001-0.2320.5780.415-0.0210.281-0.1300.1490.070
PM100.7170.1320.688-0.135-0.2641.0000.885-0.079-0.1000.500-0.049-0.2300.0250.036-0.0200.0560.093
PM2.50.8170.2250.673-0.119-0.2670.8851.000-0.085-0.0330.514-0.030-0.2840.017-0.0060.0160.0540.066
PRES0.157-0.7840.1470.171-0.411-0.079-0.0851.000-0.0810.276-0.8320.0870.015-0.0370.0010.0710.150
RAIN-0.0210.176-0.1190.012-0.001-0.100-0.033-0.0811.000-0.1640.043-0.044-0.008-0.0010.0560.0080.000
SO20.582-0.3410.552-0.359-0.2320.5000.5140.276-0.1641.000-0.3620.0310.0030.037-0.2000.0470.108
TEMP-0.2940.822-0.307-0.1200.578-0.049-0.030-0.8320.043-0.3621.0000.0520.0170.1470.1200.1050.147
WSPM-0.335-0.305-0.388-0.0360.415-0.230-0.2840.087-0.0440.0310.0521.000-0.0020.176-0.2060.1430.035
day0.0030.0250.0200.018-0.0210.0250.0170.015-0.0080.0030.017-0.0021.0000.0000.0100.0240.000
hour-0.047-0.011-0.0340.0010.2810.036-0.006-0.037-0.0010.0370.1470.1760.0001.0000.0000.1410.000
month0.0100.255-0.0510.044-0.130-0.0200.0160.0010.056-0.2000.120-0.2060.0100.0001.0000.0830.249
wd0.0820.1000.1130.1060.1490.0560.0540.0710.0080.0470.1050.1430.0240.1410.0831.0000.110
year0.0740.1580.1160.8620.0700.0930.0660.1500.0000.1080.1470.0350.0000.0000.2490.1101.000

Missing values

2024-03-08T12:17:02.491627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:17:02.968180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:17:03.552626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133108.08.06.028.0400.052.0-0.71023.0-18.80.0NNW4.4Wanliu
1220133119.09.06.028.0400.050.0-1.11023.2-18.20.0N4.7Wanliu
2320133123.06.0NaN19.0400.055.0-1.11023.5-18.20.0NNW5.6Wanliu
34201331311.030.08.014.0NaNNaN-1.41024.5-19.40.0NW3.1Wanliu
4520133143.013.09.0NaN300.054.0-2.01025.2-19.50.0N2.0Wanliu
5620133153.06.08.017.0300.054.0-2.21025.6-19.60.0N3.7Wanliu
6720133163.03.010.021.0300.052.0-2.61026.5-19.10.0NNE2.5Wanliu
7820133173.06.011.026.0300.047.0-1.61027.4-19.10.0NNW3.8Wanliu
8920133187.010.014.039.0400.036.00.11028.3-19.20.0NNW4.1Wanliu
91020133193.09.012.031.0400.046.01.21028.5-19.30.0N2.6Wanliu
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228143.017.02.06.0200.098.016.21011.7-14.40.0NW2.6Wanliu
35055350562017228155.019.03.06.0200.0100.016.21011.2-14.40.0WNW2.6Wanliu
35056350572017228169.022.04.010.0300.097.016.01011.0-14.50.0NW2.8Wanliu
350573505820172281711.028.04.011.0300.096.015.01011.0-15.30.0WNW2.5Wanliu
35058350592017228186.037.03.020.0400.084.013.01011.6-13.90.0WNW2.0Wanliu
350593506020172281911.027.04.020.0300.081.012.61011.9-14.30.0N2.0Wanliu
350603506120172282015.043.06.055.0500.045.09.41012.3-11.90.0WSW1.0Wanliu
350613506220172282113.035.07.048.0500.048.08.71012.8-13.70.0N1.1Wanliu
350623506320172282212.031.05.047.0500.050.07.81012.9-12.60.0NNE1.0Wanliu
35063350642017228237.025.06.086.0700.011.07.01012.6-11.20.0NE1.1Wanliu